Data Science Boot Up Camp
Room 056 and 052
3-8-1 Komaba, Meguro-ku, Tokyo 153-8914, Japan
Titles and Abstracts of Lectures
Philip B. Stark
Title: Foundations of Statistics and an Introduction to Statistical Inference
These lectures will complement those of Prof. Koike by focusing on foundational issues in statistics, statistical inferential thinking, the interpretation of statistical calculations, and nonparametric and exact methods. Topics will include types of uncertainty; theories of probability and their shortcomings; systematic and stochastic errors; frequentist and Bayesian approaches to estimation and inference and their shortcomings; confounding; the method of comparison; the importance of experimental/observational design; assessing estimators; interpreting p-values, confidence sets, posterior probabilities, and credible sets; common fallacies in statistical inference; the Neyman model for causal inference; interference in experiments; abstract permutation methods; pseudo-random number generation; computational implementation of permutation methods and resampling methods in Python. Examples will be drawn from physical, social, and health sciences.
Title: Introduction to Statistical Data Analysis
In this lecture we present elementary statistical data analysis methods and their implementation by R. Starting with the basic usage of R, we explain some elementary methods from multivariate analysis such as linear regression, principal component analysis and discriminant analysis and how to implement them by R. This lecture focuses on the practical implementation of the methods rather than the theoretical details.